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Activity Number: 137 - Statistical Methods for Analyzing Genetic Variants and QTLs
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #307017 Presentation
Title: Statistical Inference for Gene-Level Analysis Based on Functional Linear Models
Author(s): Olga Vsevolozhskaya and Adam Dugan* and David Fardo and Dmitri Zaykin
Companies: University of Kentucky and University of Kentucky and University of Kentucky and National Institute of Environmental Health Sciences
Keywords: gene-level test; functional data analysis; chi-squares mixture; reverse regression

Testing genetic association of SNP sets (e.g., based on genes, haplotype blocks or annotation) has become a popular alternative to standard GWAS. In the reverse regression setting where predictors are regressed on outcomes, the SNP-set can be treated as a multivariate outcome, and a test for the genetic effect on multiple phenotypic traits may be accomplished. Standard multivariate techniques (e.g., Lawley-Hotelling trace or Wilks's Lambda) perform poorly in this setting due to not accounting for genetic correlation structure / linkage disequilibrium (LD). By contrast, functional data analysis techniques explicitly incorporate LD structure. Existing procedures for testing functional covariate effects include an F-type test proposed by Shen and Faraway (2004). Its null distribution is based on a weighted mixture of central chi-squares that can be approximated by the chi-squared distribution via moment matching. Here, we propose an alternative asymptotic approximation with improved robustness and apply it to test for an association between CSF analytes and Alzheimer's disease.

Authors who are presenting talks have a * after their name.

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